AI / Machine Learning Internship - Summer 2026

CACI International•Reston, VA

1d•Hybrid

About The Position

At CACI, we are proud of our innovative workforce and the technology we deliver to our enterprise and mission customers. Want to work for a company that is recognized as a top workplace across the country? We're searching for AI/ML interns to join our team for the summer of 2026! The internship will begin in May and last 10-12 weeks. You will be working a hybrid schedule with the expectation of being on-site 2-3 days a week and working remotely on the remaining days.

Requirements

Basic Python programming knowledge
Introduction to or coursework using any ML libraries (e.g., scikit-learn, TensorFlow, PyTorch)
Familiarity with data structures and basic algorithms
AI/ML Knowledge
Understanding of fundamental ML concepts from coursework (e.g., what supervised/unsupervised learning means)
Interest in AI/ML applications and willingness to learn
Exposure to basic statistical concepts
Current sophomore or junior pursuing degree in Computer Science, Data Science, Statistics, Mathematics, or related field
Has taken or is taking introductory AI/ML orstatistics courses

Nice To Haves

Has completed a basic ML project in class or independently
Familiarity with Jupyter notebooks
Basic understanding of what LLMs or AI agents are
Curiosity about emerging AI tools and applications

Responsibilities

Developing and experimenting with generative AI applications and AI agents, including prompt engineering, LLM integration, agent workflow design, and evaluation using Python and relevant frameworks (LangChain, LlamaIndex, or similar) with guidance from senior team members
Contributing to the improvement and optimization of AI agent systems, LLM-powered applications, and generative AI pipelines, including prompt refinement, response quality evaluation, agent reasoning improvements, and experimentation with different models and orchestration approaches
Analyzing GenAI and agent-related problems and supporting the creation of solutions involving prompt design, agent architecture, retrieval-augmented generation (RAG) systems, evaluation frameworks, tool integration, and deployment strategies under the guidance of senior team members
Supporting product quality and timeliness by conducti ng agent testing, validating LLM outputs, assessing response accuracy and safety, documenting experiments and prompt iterations, participating in code reviews, and providing status updates on assigned tasks and learning progress
Exploring and evaluating emerging AI tools, models, and techniques to identify opportunities for innovation and improvement in existing AI/ML solutions
Creating documentation, demonstrations, and training materials to communicate GenAI capabilities, agent functionalities, and technical implementations to both technical and non-technical audiences